
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
import seaborn as sns


import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))


import warnings
from warnings import filterwarnings
filterwarnings('ignore')
# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session


df = pd.read_csv('C:/Users/lenovo/OneDrive/Desktop/udemyproj/hotelticketttcancellation/Predict-the-cancellation-of-Hotel-Booking-main/H_B_1.csv')


df.head()


df.shape

(119390, 32)


df.isna().sum() # get missing values

hotel                                  0
is_canceled                            0
lead_time                              0
arrival_date_year                      0
arrival_date_month                     0
arrival_date_week_number               0
arrival_date_day_of_month              0
stays_in_weekend_nights                0
stays_in_week_nights                   0
adults                                 0
children                               4
babies                                 0
meal                                   0
country                              488
market_segment                         0
distribution_channel                   0
is_repeated_guest                      0
previous_cancellations                 0
previous_bookings_not_canceled         0
reserved_room_type                     0
assigned_room_type                     0
booking_changes                        0
deposit_type                           0
agent                              16340
company                           112593
days_in_waiting_list                   0
customer_type                          0
adr                                    0
required_car_parking_spaces            0
total_of_special_requests              0
reservation_status                     0
reservation_status_date                0
dtype: int64


# deal with missing values
def data_clean(df):
    df.fillna(0, inplace=True)
    print(df.isnull().sum())


data_clean(df)

hotel                             0
is_canceled                       0
lead_time                         0
arrival_date_year                 0
arrival_date_month                0
arrival_date_week_number          0
arrival_date_day_of_month         0
stays_in_weekend_nights           0
stays_in_week_nights              0
adults                            0
children                          0
babies                            0
meal                              0
country                           0
market_segment                    0
distribution_channel              0
is_repeated_guest                 0
previous_cancellations            0
previous_bookings_not_canceled    0
reserved_room_type                0
assigned_room_type                0
booking_changes                   0
deposit_type                      0
agent                             0
company                           0
days_in_waiting_list              0
customer_type                     0
adr                               0
required_car_parking_spaces       0
total_of_special_requests         0
reservation_status                0
reservation_status_date           0
dtype: int64


df.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


list = ['adults', 'children', 'babies']

for i in list:
    print('{} has unique values as {}'.format(i, df[i].unique()))

adults has unique values as [ 2  1  3  4 40 26 50 27 55  0 20  6  5 10]
children has unique values as [ 0.  1.  2. 10.  3.]
babies has unique values as [ 0  1  2 10  9]


# wrong entries where adults, childern and babies are '0' at a time
filter = (df['children']==0) & (df['adults']==0) & (df['babies']==0) 
df[filter]


# data after cleaning
data = df[~filter] # negation of filter i.e., data without above data
data.head()


country_wise = data[data['is_canceled'] == 0]['country'].value_counts()
country_wise

PRT    20977
GBR     9668
FRA     8468
ESP     6383
DEU     6067
       ...  
NAM        1
ATF        1
PYF        1
DMA        1
BFA        1
Name: country, Length: 166, dtype: int64


# make a dataframe of above data
country_wise_df = country_wise.reset_index()
country_wise_df.columns = ['country', 'no. of guests']
country_wise_df


import folium
from folium.plugins import HeatMap
basemap = folium.Map()

import plotly.express as px
map_guests = px.choropleth(country_wise_df,
             locations=country_wise_df['country'],
             color=country_wise_df['no. of guests'],
             hover_name=country_wise_df['country'],
             title='Home country of guests')
map_guests.show()


data2 = data[data['is_canceled']==0]
data2.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


plt.figure(figsize=(12,8))
sns.boxplot(x='reserved_room_type', y='adr', hue='hotel', data=data2)
plt.title('Price of rooms types per night & per person')
plt.xlabel('Room type')
plt.ylabel('Price(Euro)')
plt.legend()
plt.show()


data_resort = data[(data['hotel']=='Resort Hotel') & (data['is_canceled']==0)]
data_city = data[(data['hotel']=='City Hotel') & (data['is_canceled']==0)]


data_resort.head()


resort_hotel_df = data_resort.groupby(['arrival_date_month'])['adr'].mean().reset_index()
resort_hotel_df


city_hotel_df = data_city.groupby(['arrival_date_month'])['adr'].mean().reset_index()
city_hotel_df


# merge both data 
final_df = resort_hotel_df.merge(city_hotel_df, on='arrival_date_month')
final_df.columns=['month', 'price_for_resort_hotel', 'price_for_city_hotel'] # rename columns
final_df


# python modules for sorting the month, weekday etc
!pip install sort_dataframeby_monthorweek
!pip install sorted-months-weekdays

Requirement already satisfied: sort_dataframeby_monthorweek in c:\users\lenovo\anaconda3\lib\site-packages (0.4)
Requirement already satisfied: sorted-months-weekdays in c:\users\lenovo\anaconda3\lib\site-packages (0.2)


import sort_dataframeby_monthorweek as sd

def sort_data(df, column):
    return sd.Sort_Dataframeby_Month(df, column)


final_df = sort_data(final_df, 'month')
final_df


final_df.columns

Index(['month', 'price_for_resort_hotel', 'price_for_city_hotel'], dtype='object')


# line plot
px.line(final_df, x='month', y=['price_for_resort_hotel', 'price_for_city_hotel'], 
        title='Room price per night over the months')


rush_resort_df = data_resort['arrival_date_month'].value_counts().reset_index()
rush_resort_df.columns = ['month', 'no. of guests']
rush_resort_df


rush_city_df = data_city['arrival_date_month'].value_counts().reset_index()
rush_city_df.columns = ['month', 'no. of guests']
rush_city_df


final_rush_df = rush_resort_df.merge(rush_city_df, on='month')
final_rush_df.columns = ['month', 'no. of guests in resort', 'no. of guests in city hotel']
final_rush_df


final_rush_df = sort_data(final_rush_df, 'month')
final_rush_df


# line plot
px.line(final_rush_df, x='month', y=['no. of guests in resort', 'no. of guests in city hotel'], 
        title='Total number of guests per months')


data.corr()


# find the correlation with "is_canceled" because it is depended feature 
# (how all other variable are going to be depended on is_canceled?)

correlation = data.corr()['is_canceled']
correlation

is_canceled                       1.000000
lead_time                         0.292876
arrival_date_year                 0.016622
arrival_date_week_number          0.008315
arrival_date_day_of_month        -0.005948
stays_in_weekend_nights          -0.001323
stays_in_week_nights              0.025542
adults                            0.058182
children                          0.004851
babies                           -0.032569
is_repeated_guest                -0.083745
previous_cancellations            0.110139
previous_bookings_not_canceled   -0.057365
booking_changes                  -0.144832
agent                            -0.046770
company                          -0.083594
days_in_waiting_list              0.054301
adr                               0.046492
required_car_parking_spaces      -0.195701
total_of_special_requests        -0.234877
Name: is_canceled, dtype: float64


correlation.abs().sort_values(ascending=False)

is_canceled                       1.000000
lead_time                         0.292876
total_of_special_requests         0.234877
required_car_parking_spaces       0.195701
booking_changes                   0.144832
previous_cancellations            0.110139
is_repeated_guest                 0.083745
company                           0.083594
adults                            0.058182
previous_bookings_not_canceled    0.057365
days_in_waiting_list              0.054301
agent                             0.046770
adr                               0.046492
babies                            0.032569
stays_in_week_nights              0.025542
arrival_date_year                 0.016622
arrival_date_week_number          0.008315
arrival_date_day_of_month         0.005948
children                          0.004851
stays_in_weekend_nights           0.001323
Name: is_canceled, dtype: float64


data.groupby('is_canceled')['reservation_status'].value_counts()

is_canceled  reservation_status
0            Check-Out             75011
1            Canceled              42993
             No-Show                1206
Name: reservation_status, dtype: int64


data.columns

Index(['hotel', 'is_canceled', 'lead_time', 'arrival_date_year',
       'arrival_date_month', 'arrival_date_week_number',
       'arrival_date_day_of_month', 'stays_in_weekend_nights',
       'stays_in_week_nights', 'adults', 'children', 'babies', 'meal',
       'country', 'market_segment', 'distribution_channel',
       'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'reserved_room_type',
       'assigned_room_type', 'booking_changes', 'deposit_type', 'agent',
       'company', 'days_in_waiting_list', 'customer_type', 'adr',
       'required_car_parking_spaces', 'total_of_special_requests',
       'reservation_status', 'reservation_status_date'],
      dtype='object')


# exclude numerical columns which are not going to contribute much
num_not = ['days_in_waiting_list', 'arrival_date_year']


# get numerical features
num_features = [col for col in data.columns if data[col].dtype != 'O' and col not in num_not]
num_features

['is_canceled',
 'lead_time',
 'arrival_date_week_number',
 'arrival_date_day_of_month',
 'stays_in_weekend_nights',
 'stays_in_week_nights',
 'adults',
 'children',
 'babies',
 'is_repeated_guest',
 'previous_cancellations',
 'previous_bookings_not_canceled',
 'booking_changes',
 'agent',
 'company',
 'adr',
 'required_car_parking_spaces',
 'total_of_special_requests']


# exclude the categorical features that are not going to contribute much
cat_not = ['arrival_date_year', 'assigned_room_type', 'booking_changes', 'reservation_status', 'country', 'days_in_waiting_list']


# get catrgorical features
cat_features = [col for col in data.columns if data[col].dtype == 'O' and col not in cat_not]
cat_features

['hotel',
 'arrival_date_month',
 'meal',
 'market_segment',
 'distribution_channel',
 'reserved_room_type',
 'deposit_type',
 'customer_type',
 'reservation_status_date']


data_cat = data[cat_features]
data_cat.head()


data_cat.dtypes

hotel                      object
arrival_date_month         object
meal                       object
market_segment             object
distribution_channel       object
reserved_room_type         object
deposit_type               object
customer_type              object
reservation_status_date    object
dtype: object


data_cat['reservation_status_date'] = pd.to_datetime(data_cat['reservation_status_date'])


# derived features
data_cat['year'] = data_cat['reservation_status_date'].dt.year
data_cat['month'] = data_cat['reservation_status_date'].dt.month
data_cat['day'] = data_cat['reservation_status_date'].dt.day

data_cat['cancellation'] = data['is_canceled']


# drop a feature because derived features already carries its information
data_cat.drop('reservation_status_date', axis=1, inplace=True) # drop vertically and update dataframe


data_cat.head()


data_cat['market_segment'].unique()

array(['Direct', 'Corporate', 'Online TA', 'Offline TA/TO',
       'Complementary', 'Groups', 'Undefined', 'Aviation'], dtype=object)


cols = data_cat.columns[0:8] # each columns except 'cancellation'


# mean encoding, for example
data_cat.groupby(['hotel'])['cancellation'].mean() # it will replace the resort hotel with value '0.277674'

hotel
City Hotel      0.417859
Resort Hotel    0.277674
Name: cancellation, dtype: float64


#  Each categorical feature has its value which will be used by ML model
# and convert into dictionary so it can be easily mapped

for col in cols:
    print(data_cat.groupby([col])['cancellation'].mean().to_dict())
    print('\n')

{'City Hotel': 0.4178593534858457, 'Resort Hotel': 0.27767373336329815}


{'April': 0.40783534934103627, 'August': 0.37782266791717767, 'December': 0.35034768456872317, 'February': 0.3344510680576254, 'January': 0.305016044587063, 'July': 0.37464409996836445, 'June': 0.41485954799158203, 'March': 0.32227682227682225, 'May': 0.3970288624787776, 'November': 0.31309998523113275, 'October': 0.38090966179241054, 'September': 0.3919047619047619}


{'BB': 0.3741055553146277, 'FB': 0.5989974937343359, 'HB': 0.3446534790427445, 'SC': 0.3740638923120675, 'Undefined': 0.2446535500427716}


{'Aviation': 0.22127659574468084, 'Complementary': 0.12225274725274725, 'Corporate': 0.18761832639151838, 'Direct': 0.15371165156572883, 'Groups': 0.6110858471022181, 'Offline TA/TO': 0.3433132081713671, 'Online TA': 0.3675897035881435, 'Undefined': 1.0}


{'Corporate': 0.22056833558863329, 'Direct': 0.1748682499486688, 'GDS': 0.19170984455958548, 'TA/TO': 0.41059846547314577, 'Undefined': 0.8}


{'A': 0.39156661581638, 'B': 0.3291479820627803, 'C': 0.3308270676691729, 'D': 0.31810834767193286, 'E': 0.2926829268292683, 'F': 0.304077401520387, 'G': 0.3647227533460803, 'H': 0.40765391014975044, 'L': 0.3333333333333333}


{'No Deposit': 0.28401987344559215, 'Non Refund': 0.9936244601357374, 'Refundable': 0.2222222222222222}


{'Contract': 0.3099214145383104, 'Group': 0.10104529616724739, 'Transient': 0.40786356117841654, 'Transient-Party': 0.25450414540816324}


# mapping the data into dataframe
for col in cols:
    dict = data_cat.groupby([col])['cancellation'].mean().to_dict()
    data_cat[col] = data_cat[col].map(dict)


data_cat.head()


dataframe = pd.concat([data_cat, data[num_features]], axis=1)


dataframe.columns

Index(['hotel', 'arrival_date_month', 'meal', 'market_segment',
       'distribution_channel', 'reserved_room_type', 'deposit_type',
       'customer_type', 'year', 'month', 'day', 'cancellation', 'is_canceled',
       'lead_time', 'arrival_date_week_number', 'arrival_date_day_of_month',
       'stays_in_weekend_nights', 'stays_in_week_nights', 'adults', 'children',
       'babies', 'is_repeated_guest', 'previous_cancellations',
       'previous_bookings_not_canceled', 'booking_changes', 'agent', 'company',
       'adr', 'required_car_parking_spaces', 'total_of_special_requests'],
      dtype='object')


dataframe.drop('cancellation', axis=1, inplace=True) # we had both 'cancellation' and 'is_canceled'
dataframe


dataframe.head()


def handle_outlier(col):
    dataframe[col] = np.log1p(dataframe[col])


# for lead_time
sns.distplot(dataframe['lead_time'])

<AxesSubplot:xlabel='lead_time', ylabel='Density'>


handle_outlier('lead_time')
sns.distplot(dataframe['lead_time'])

<AxesSubplot:xlabel='lead_time', ylabel='Density'>


# for adr
sns.distplot(dataframe['adr'])

<AxesSubplot:xlabel='adr', ylabel='Density'>


handle_outlier('adr')
sns.distplot(dataframe['adr'].dropna())

<AxesSubplot:xlabel='adr', ylabel='Density'>


dataframe.isnull().sum()

hotel                             0
arrival_date_month                0
meal                              0
market_segment                    0
distribution_channel              0
reserved_room_type                0
deposit_type                      0
customer_type                     0
year                              0
month                             0
day                               0
is_canceled                       0
lead_time                         0
arrival_date_week_number          0
arrival_date_day_of_month         0
stays_in_weekend_nights           0
stays_in_week_nights              0
adults                            0
children                          0
babies                            0
is_repeated_guest                 0
previous_cancellations            0
previous_bookings_not_canceled    0
booking_changes                   0
agent                             0
company                           0
adr                               1
required_car_parking_spaces       0
total_of_special_requests         0
dtype: int64


dataframe.dropna(inplace=True) # drop and update the dataframe


x = dataframe.drop('is_canceled', axis=1) # independent features
y = dataframe['is_canceled'] # dependent feature


x.shape

(119209, 28)


y.shape

(119209,)


from sklearn.linear_model import Lasso
from sklearn.feature_selection import SelectFromModel


feature_sel_model = SelectFromModel(Lasso(alpha=0.005,random_state=0))


feature_sel_model.fit(x,y)

SelectFromModel(estimator=Lasso(alpha=0.005, random_state=0))


feature_sel_model.get_support() # if a feature is going to contribute or not

array([False, False, False, False, False, False,  True, False,  True,
        True,  True,  True,  True, False, False, False,  True,  True,
       False, False,  True, False,  True, False,  True,  True,  True,
        True])


cols = x.columns
selected_features = cols[feature_sel_model.get_support()] # using as a filter, feature corresponding to True will be filtered
print('total features: {}'.format(x.shape[1]))
print('selected features: {}'.format(len(selected_features)))

total features: 28
selected features: 14


# update x
x = x[selected_features]


x.shape

(119209, 14)


from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression

X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.25, random_state=42)
logreg = LogisticRegression(solver='liblinear')

logreg.fit(X_train,y_train)

LogisticRegression(solver='liblinear')


y_pred = logreg.predict(X_test)
y_pred

array([0, 0, 0, ..., 0, 0, 0], dtype=int64)


from sklearn.metrics import confusion_matrix

confusion_matrix(y_test, y_pred)

array([[18129,   555],
       [ 5389,  5730]], dtype=int64)


from sklearn.metrics import accuracy_score

accuracy_score(y_test, y_pred)

0.8005569909069556


from sklearn.model_selection import cross_val_score


score = cross_val_score(logreg, x, y, cv=10)
score.mean()

0.7791445057197017


from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier


models = []

models.append(('LogisticRegression', LogisticRegression(solver='liblinear')))
models.append(('Naive bayes', GaussianNB()))
models.append(('RandomForest', RandomForestClassifier()))
models.append(('Decision Tree', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))


models

[('LogisticRegression', LogisticRegression(solver='liblinear')),
 ('Naive bayes', GaussianNB()),
 ('RandomForest', RandomForestClassifier()),
 ('Decision Tree', DecisionTreeClassifier()),
 ('KNN', KNeighborsClassifier())]


for name, model in models:
    print(name)
    model.fit(X_train, y_train)
    
    predictions = model.predict(X_test)
    print(confusion_matrix(predictions, y_test))
    
    print(accuracy_score(predictions, y_test))
    print('\n')

LogisticRegression
[[18129  5389]
 [  555  5730]]
0.8005569909069556


Naive bayes
[[ 8288  1138]
 [10396  9981]]
0.6129919806730866


RandomForest
[[18558  1203]
 [  126  9916]]
0.9554071737744523


Decision Tree
[[17799   781]
 [  885 10338]]
0.9440995872898702


KNN
[[18531  1397]
 [  153  9722]]
0.9479918129047411

	month	price_for_resort_hotel	price_for_city_hotel
0	April	75.867816	111.962267
1	August	181.205892	118.674598
2	December	68.410104	88.401855
3	February	54.147478	86.520062
4	January	48.761125	82.330983
5	July	150.122528	115.818019
6	June	107.974850	117.874360
7	March	57.056838	90.658533
8	May	76.657558	120.669827
9	November	48.706289	86.946592
10	October	61.775449	102.004672
11	September	96.416860	112.776582

	month	price_for_resort_hotel	price_for_city_hotel
0	January	48.761125	82.330983
1	February	54.147478	86.520062
2	March	57.056838	90.658533
3	April	75.867816	111.962267
4	May	76.657558	120.669827
5	June	107.974850	117.874360
6	July	150.122528	115.818019
7	August	181.205892	118.674598
8	September	96.416860	112.776582
9	October	61.775449	102.004672
10	November	48.706289	86.946592
11	December	68.410104	88.401855

	month	no. of guests in resort	no. of guests in city hotel
0	August	3257	5367
1	July	3137	4770
2	October	2575	4326
3	March	2571	4049
4	April	2550	4010
5	May	2535	4568
6	February	2308	3051
7	September	2102	4283
8	June	2037	4358
9	December	2014	2377
10	November	1975	2676
11	January	1866	2249

	month	no. of guests in resort	no. of guests in city hotel
0	January	1866	2249
1	February	2308	3051
2	March	2571	4049
3	April	2550	4010
4	May	2535	4568
5	June	2037	4358
6	July	3137	4770
7	August	3257	5367
8	September	2102	4283
9	October	2575	4326
10	November	1975	2676
11	December	2014	2377

	is_canceled	lead_time	arrival_date_year	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	children	babies	is_repeated_guest	previous_cancellations	previous_bookings_not_canceled	booking_changes	agent	company	days_in_waiting_list	adr	required_car_parking_spaces	total_of_special_requests
is_canceled	1.000000	0.292876	0.016622	0.008315	-0.005948	-0.001323	0.025542	0.058182	0.004851	-0.032569	-0.083745	0.110139	-0.057365	-0.144832	-0.046770	-0.083594	0.054301	0.046492	-0.195701	-0.234877
lead_time	0.292876	1.000000	0.040334	0.127046	0.002306	0.085985	0.166892	0.117575	-0.037878	-0.021003	-0.123209	0.086025	-0.073599	0.002230	-0.013114	-0.085854	0.170008	-0.065018	-0.116624	-0.095949
arrival_date_year	0.016622	0.040334	1.000000	-0.540373	-0.000121	0.021694	0.031203	0.030266	0.054710	-0.013192	0.010281	-0.119905	0.029234	0.031416	0.056438	0.033682	-0.056348	0.198429	-0.013812	0.108610
arrival_date_week_number	0.008315	0.127046	-0.540373	1.000000	0.066572	0.018629	0.016047	0.026567	0.005556	0.010417	-0.031125	0.035493	-0.021009	0.006311	-0.018225	-0.032912	0.022677	0.076281	0.001980	0.026202
arrival_date_day_of_month	-0.005948	0.002306	-0.000121	0.066572	1.000000	-0.016225	-0.028362	-0.001754	0.014550	-0.000235	-0.006471	-0.027027	-0.000306	0.011266	0.000159	0.003667	0.022532	0.030291	0.008569	0.003026
stays_in_weekend_nights	-0.001323	0.085985	0.021694	0.018629	-0.016225	1.000000	0.494175	0.094759	0.046135	0.018607	-0.086009	-0.012769	-0.042859	0.050191	0.162411	-0.080783	-0.054399	0.050670	-0.018520	0.073124
stays_in_week_nights	0.025542	0.166892	0.031203	0.016047	-0.028362	0.494175	1.000000	0.096214	0.044652	0.020373	-0.095302	-0.013976	-0.048873	0.080018	0.196777	-0.044437	-0.002026	0.066847	-0.024933	0.068738
adults	0.058182	0.117575	0.030266	0.026567	-0.001754	0.094759	0.096214	1.000000	0.029409	0.017890	-0.140973	-0.007070	-0.108856	-0.041472	0.023370	-0.166182	-0.008365	0.224253	0.014438	0.123353
children	0.004851	-0.037878	0.054710	0.005556	0.014550	0.046135	0.044652	0.029409	1.000000	0.023999	-0.032475	-0.024755	-0.021078	0.051000	0.050461	-0.042554	-0.033293	0.325057	0.056247	0.081747
babies	-0.032569	-0.021003	-0.013192	0.010417	-0.000235	0.018607	0.020373	0.017890	0.023999	1.000000	-0.008813	-0.007509	-0.006552	0.085605	0.030235	-0.009426	-0.010627	0.029043	0.037389	0.097939
is_repeated_guest	-0.083745	-0.123209	0.010281	-0.031125	-0.006471	-0.086009	-0.095302	-0.140973	-0.032475	-0.008813	1.000000	0.082740	0.420642	0.013044	-0.051584	0.161871	-0.022057	-0.130807	0.077928	0.012963
previous_cancellations	0.110139	0.086025	-0.119905	0.035493	-0.027027	-0.012769	-0.013976	-0.007070	-0.024755	-0.007509	0.082740	1.000000	0.152570	-0.027261	-0.018251	-0.001110	0.005941	-0.065974	-0.018540	-0.048488
previous_bookings_not_canceled	-0.057365	-0.073599	0.029234	-0.021009	-0.000306	-0.042859	-0.048873	-0.108856	-0.021078	-0.006552	0.420642	0.152570	1.000000	0.011963	-0.046348	0.111220	-0.009416	-0.072335	0.047506	0.037775
booking_changes	-0.144832	0.002230	0.031416	0.006311	0.011266	0.050191	0.080018	-0.041472	0.051000	0.085605	0.013044	-0.027261	0.011963	1.000000	0.038555	0.089768	-0.011916	0.026601	0.067490	0.055003
agent	-0.046770	-0.013114	0.056438	-0.018225	0.000159	0.162411	0.196777	0.023370	0.050461	0.030235	-0.051584	-0.018251	-0.046348	0.038555	1.000000	-0.121333	-0.041182	0.015711	0.119282	0.060783
company	-0.083594	-0.085854	0.033682	-0.032912	0.003667	-0.080783	-0.044437	-0.166182	-0.042554	-0.009426	0.161871	-0.001110	0.111220	0.089768	-0.121333	1.000000	-0.022944	-0.127641	0.038638	-0.090790
days_in_waiting_list	0.054301	0.170008	-0.056348	0.022677	0.022532	-0.054399	-0.002026	-0.008365	-0.033293	-0.010627	-0.022057	0.005941	-0.009416	-0.011916	-0.041182	-0.022944	1.000000	-0.040859	-0.030601	-0.082755
adr	0.046492	-0.065018	0.198429	0.076281	0.030291	0.050670	0.066847	0.224253	0.325057	0.029043	-0.130807	-0.065974	-0.072335	0.026601	0.015711	-0.127641	-0.040859	1.000000	0.056510	0.172308
required_car_parking_spaces	-0.195701	-0.116624	-0.013812	0.001980	0.008569	-0.018520	-0.024933	0.014438	0.056247	0.037389	0.077928	-0.018540	0.047506	0.067490	0.119282	0.038638	-0.030601	0.056510	1.000000	0.082718
total_of_special_requests	-0.234877	-0.095949	0.108610	0.026202	0.003026	0.073124	0.068738	0.123353	0.081747	0.097939	0.012963	-0.048488	0.037775	0.055003	0.060783	-0.090790	-0.082755	0.172308	0.082718	1.000000

DATA CLEANING¶

Analysing home country of guests¶

Analysing prices of hotel across year for Resort Hotel and City Hotel¶

(how much price varies over year?)¶

Analysing the demand of hotels¶

(highest number of guests in which season?)¶

Select important features using ML (correlation matrix)¶

Extract derived features from data¶

Handling Categorical features (Mean Encoding)¶

Handling Outliers¶

Applying techniques of Feature Importance¶

To select most important features for ML model¶

Apply Logestic Regression¶

Apply Cross Validation¶

Apply multiple ML algorithms¶

	hotel	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_week_nights	adults	...	deposit_type	agent	company	customer_type	adr	total_of_special_requests	reservation_status	reservation_status_date
0	Resort Hotel	342	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	7/1/2015
1	Resort Hotel	737	2015	July	27	1	0	2	...	No Deposit	NaN	NaN	Transient	0.0	0	Check-Out	7/1/2015
2	Resort Hotel	7	2015	July	27	1	1	1	...	No Deposit	NaN	NaN	Transient	75.0	0	Check-Out	7/2/2015
3	Resort Hotel	13	2015	July	27	1	1	1	...	No Deposit	304.0	NaN	Transient	75.0	0	Check-Out	7/2/2015
4	Resort Hotel	14	2015	July	27	1	2	2	...	No Deposit	240.0	NaN	Transient	98.0	1	Check-Out	7/3/2015

	hotel	is_canceled	lead_time	arrival_date_year	arrival_date_month	arrival_date_week_number	arrival_date_day_of_month	stays_in_weekend_nights	stays_in_week_nights	adults	...	deposit_type	agent	company	days_in_waiting_list	customer_type	adr	required_car_parking_spaces	total_of_special_requests	reservation_status	reservation_status_date
2224	Resort Hotel	0	1	2015	October	41	6	0	3	0	...	No Deposit	0.0	174.0	0	Transient-Party	0.00	0	0	Check-Out	10/6/2015
2409	Resort Hotel	0	0	2015	October	42	12	0	0	0	...	No Deposit	0.0	174.0	0	Transient	0.00	0	0	Check-Out	10/12/2015
3181	Resort Hotel	0	36	2015	November	47	20	1	2	0	...	No Deposit	38.0	0.0	0	Transient-Party	0.00	0	0	Check-Out	11/23/2015
3684	Resort Hotel	0	165	2015	December	53	30	1	4	0	...	No Deposit	308.0	0.0	122	Transient-Party	0.00	0	0	Check-Out	1/4/2016
3708	Resort Hotel	0	165	2015	December	53	30	2	4	0	...	No Deposit	308.0	0.0	122	Transient-Party	0.00	0	0	Check-Out	1/5/2016
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
115029	City Hotel	0	107	2017	June	26	27	0	3	0	...	No Deposit	7.0	0.0	0	Transient	100.80	0	0	Check-Out	6/30/2017
115091	City Hotel	0	1	2017	June	26	30	0	1	0	...	No Deposit	0.0	0.0	0	Transient	0.00	1	1	Check-Out	7/1/2017
116251	City Hotel	0	44	2017	July	28	15	1	1	0	...	No Deposit	425.0	0.0	0	Transient	73.80	0	0	Check-Out	7/17/2017
116534	City Hotel	0	2	2017	July	28	15	2	5	0	...	No Deposit	9.0	0.0	0	Transient-Party	22.86	0	1	Check-Out	7/22/2017
117087	City Hotel	0	170	2017	July	30	27	0	2	0	...	No Deposit	52.0	0.0	0	Transient	0.00	0	0	Check-Out	7/29/2017

	country	no. of guests
0	PRT	20977
1	GBR	9668
2	FRA	8468
3	ESP	6383
4	DEU	6067
...	...	...
161	NAM	1
162	ATF	1
163	PYF	1
164	DMA	1
165	BFA	1

	hotel	arrival_date_month	meal	market_segment	distribution_channel	reserved_room_type	deposit_type	customer_type	reservation_status_date
0	Resort Hotel	July	BB	Direct	Direct	C	No Deposit	Transient	7/1/2015
1	Resort Hotel	July	BB	Direct	Direct	C	No Deposit	Transient	7/1/2015
2	Resort Hotel	July	BB	Direct	Direct	A	No Deposit	Transient	7/2/2015
3	Resort Hotel	July	BB	Corporate	Corporate	A	No Deposit	Transient	7/2/2015
4	Resort Hotel	July	BB	Online TA	TA/TO	A	No Deposit	Transient	7/3/2015

	hotel	arrival_date_month	meal	market_segment	distribution_channel	reserved_room_type	deposit_type	customer_type	year	month	day
0	0.277674	0.374644	0.374106	0.153712	0.174868	0.330827	0.28402	0.407864	2015	7	1
1	0.277674	0.374644	0.374106	0.153712	0.174868	0.330827	0.28402	0.407864	2015	7	1
2	0.277674	0.374644	0.374106	0.153712	0.174868	0.391567	0.28402	0.407864	2015	7	2
3	0.277674	0.374644	0.374106	0.187618	0.220568	0.391567	0.28402	0.407864	2015	7	2
4	0.277674	0.374644	0.374106	0.367590	0.410598	0.391567	0.28402	0.407864	2015	7	3